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Economic Models

Code: 40097
Credits: 15
2026/2027
Degree programme Type Course
Economic Analysis OB 1

Contact lecturer

Name :
Javier Fernandez Blanco
Email :
javier.fernandez@uab.cat

Teaching staff

Michael David Creel
Jordi Masso Carreras

Teaching staff (external to UAB)

Lidia Farré

Group languages

You can consult this information at the end of the document.

Prerequisites

No specific prerequisits.

Objectives

The goal of the first part of the module is for students to learn standard concepts of non-cooperative and cooperative Game Theory at a graduate level.

In the second and third parts of the module the goal is for students to learn how  to  analyze,  interpret  and  organize  economic  data  with  advanced  statistical  and  econometric techniques.  The  student will also become familiar with the use of econometric software  packages.     

Learning outcomes

  • CA06 (Communicate econometric results and implications to diverse audiences.) Communicate econometric results and implications to diverse audiences.
  • CA07 (Gather economic datasets following good replication practices.) Gather economic datasets following good replication practices.
  • CA08 (Formulate complex dynamic questions for resolution with dynamic programming.) Formulate complex dynamic questions for resolution with dynamic programming.
  • CA09 (Review innovative methodologies by comparing them with current standards.) Review innovative methodologies by comparing them with current standards.
  • KA12 (Describe the principles of estimation theory and the criteria for accepting hypotheses.) Describe the principles of estimation theory and the criteria for accepting hypotheses.
  • KA13 (List the linear models (OLS/GLS) and the Maximum Likelihood method, along with their assumptions.) List the linear models (OLS/GLS) and the Maximum Likelihood method, along with their assumptions.
  • KA14 (Identify IV, GMM, panel, Bayesian, simulation, nonparametric, and quantile techniques.) Identify IV, GMM, panel, Bayesian, simulation, nonparametric, and quantile techniques.
  • KA15 (Recognise the use of the overlapping generations model in policy analysis.) Recognise the use of the overlapping generations model in policy analysis.
  • SA07 (Use estimation methods in statistical packages on actual data.) Use estimation methods in statistical packages on actual data.
  • SA08 (Structure a dynamic model as a system of equations for its programming.) Structure a dynamic model as a system of equations for its programming.
  • SA09 (Evaluate the limits of basic estimators using Monte Carlo simulations.) Evaluate the limits of basic estimators using Monte Carlo simulations.
  • SA10 (Criticise the power and bias of different hypothesis tests.) Criticise the power and bias of different hypothesis tests.
  • SA11 (Develop routines for reproducible econometric analysis.) Develop routines for reproducible econometric analysis.

Contents

I.Game Theory


1.Introduction to Game Theory and Some Examples


2.Games in Normal Form


3.Games in Extensive Form


4.Nash Equilibrium and Related Issues


5.Repeated Games


6.Games of Incomplete Information


7.Bargaining Theory


8.Cooperative Games


 


II.Econometrics  I  


1. Introduction to econometric analysis


2. Ordinary least squares


3. OLS and finite sample theory


4. OLS and large sample theory


5. Nonspherical disturbances


6. Endogeneity


 


III.Econometrics  II  


1. Extremum estimation and numerical optimization


2. Maximum likelihood


3. Generalized Method of Moments  


4. Introduction to  time series  analysis  


5. Additional topics  in econometrics  


For a detailed description of the content of this module go to https://sites.google.com/view/idea-program/master-program .


 

Learning activities and methodology

Title Hours ECTS Learning outcomes
Problems sets, tutorials 75 3
Theory classes 112.5 4.5
Personal study, study groups, textbook readings, article readings 187.5 7.5

The course will consist of sessions where the instructor presents the material, and sessions specifically dedicated to problem solving. Students are encouraged to form study groups to discuss assignments and readings.

The proposed methodology may undergo some modifications according to the restrictions imposed by the health authorities on on-campus courses.

 

Annotation: within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.

Assessment

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
Exam Part II 26% 0 0 CA06, CA07, CA08, CA09, KA12, KA13, KA14, KA15, SA07, SA08, SA09, SA10, SA11
Class Attendance and Problem sets and assignments 22% 0 0 CA06, CA07, CA08, CA09, KA12, KA13, KA14, KA15, SA07, SA08, SA09, SA10, SA11
Exam Part I 26% 0 0 CA06, CA07, CA08, CA09, KA12, KA13, KA14, KA15, SA07, SA08, SA09, SA10, SA11
Exam Part III 26% 0 0 CA06, CA07, CA08, CA09, KA12, KA13, KA14, KA15, SA07, SA08, SA09, SA10, SA11

1. CONTINUOUS EVALUATION

  Exam Part I

26%  

 Exam Part II

26%  

 Exam Part III

26%  

Problem  sets,   assignments  & Class  attendance    and    active    participation

22%  

The proposed evaluation activities may undergo some changes according to the restrictions imposed by the health authorities on on-campus courses. 

In this course, the use of Artificial Intelligence (AI) technologies is not permitted in any of its phases. Any work that includes fragments generated with AI will be considered a breach of academic honesty and may result in a partial or total penalty to the activity's grade, or more severe sanctions in serious cases.

2. THIS MODUL CONTEMPLATES A COMPREHENSIVE EVALUATION option:

COMPREHENSIVE EVALUATION (Art. 265 of the UAB Academic Regulations)

By requesting the comprehensive evaluation the student waives the option of continuous evaluation.

The comprehensive  evaluation must be requested at the Academic Management (Gestió acadèmica) of the Campus where the degree/master's degree is taught. The request must be filed according to the procedure and the deadline  established by the administrative calendar of the Faculty of Economics and Business.

Attendance :

  • Student  attendance  is mandatory on the day of the comprehensive assessment. The date will be the same as that of the final exam of the semester as per the evaluation calendar published by the Faculty of Economics and Business and approved by the Faculty's Teaching and Academic Affairs Committee. The duration of the comprehensive assessment must be specified in the characteristics of such activity.
  • 100% of the evaluation evidences must be handed in by the student on the day of the comprehensive assessment.
  • The evaluation evidences carried out in person by the student on the same day of the comprehensive assessment must have a minimum weight of 70%.

The following information referring to the characteristics of the comprehensive assessment must be included. We suggest incorporating the following table:

Evidence Type (1)

Weight in the final assessment (%) (2)

Duration of the activity

Is the activity that corresponds to this evaluation evidence to be carried out in person on the  date scheduled for the comprehensive evaluation? (YES/NO) (3)

EXAM

80%

 

 YES

 LAB TEST

 20%

 

 YES

 

 

 

 

TOTAL

100%

 

 

 

(1)    Descriptive title of each piece of evidence (exam, problem sets solving, case analysis, activity carried out using specific software that the student is expected to know,...)

(2)    Weight of the evidence in the final mark of the subject (specify the percentages of each evaluation evidence that the student must undertake)

(3)    For each piece of evidence: Is the activity that corresponds to this evaluation evidence to be carried out in person on the  date scheduled for the comprehensive evaluation? (YES/NO)

Bibliography

Game theory:

Fudenberg and J. Tirole (1991). Game Theory. MIT Press.

Gibbons (1992). A Primer in Game Theory. Harvester Wheatsheal.

Luce and H. Raiffa (1957). Games and Decisions. Wiley.

Mas-Colell, M. Whinston and J. Green (1995). Microeconomic Theory. Oxford University Press.

Moulin (1986). Game Theory for the Social Sciences (second edition). New York University Press.

Moulin (1988). Axioms of Cooperative Decision Making. Cambridge University Press (Econometric Society Monographs).

Myerson (1991). Game Theory: Analysis of Conflict. Harvard University Press.

Osborne and A. Rubinstein (1994). A Course in Game Theory. MIT Press.

Owen (1982). Game Theory (second edition). Academic Press.

Shubik (1984). Game Theory in the Social Sciences. MIT Press.

Vega-Redondo (2003). Economics and the Theory of Games. Cambridge University Press.

 

Econometrics I and II

Cameron, A.C. and P.K. Trivedi, Microeconometrics - Methods and Applications

Davidson, R. and J.G. MacKinnon, Econometric Theory and Methods

Gallant, A.R., An Introduction to Econometric Theor

Greene, W.H. Econometric Analysis, Pearson Prentice Hall.

Hamilton, J.D., Time Series Analysis

Hayashi, F.,Econometrics, Princeton Univesrity Press.

Wooldridge. Econometric Analysis of Cross Section and Panel Data, MIT Press, Cambridge- Mass, USA.

 

Additional references will be provided during the course.

Software

  • Matlab
  • R
  • Phyton
  • Stata

Course groups and languages

The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE

Type of teaching Group Language Semester Shift
(TEm) Theory (master) 30 English second semester morning-mixed
(PLABm) Practical laboratories (master) 30 English second semester morning-mixed